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Hands-On Big Data Analytics with PySpark

You're reading from  Hands-On Big Data Analytics with PySpark

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781838644130
Pages 182 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Rudy Lai Rudy Lai
Profile icon Rudy Lai
Bartłomiej Potaczek Bartłomiej Potaczek
Profile icon Bartłomiej Potaczek
View More author details

Table of Contents (15) Chapters

Preface Installing Pyspark and Setting up Your Development Environment Getting Your Big Data into the Spark Environment Using RDDs Big Data Cleaning and Wrangling with Spark Notebooks Aggregating and Summarizing Data into Useful Reports Powerful Exploratory Data Analysis with MLlib Putting Structure on Your Big Data with SparkSQL Transformations and Actions Immutable Design Avoiding Shuffle and Reducing Operational Expenses Saving Data in the Correct Format Working with the Spark Key/Value API Testing Apache Spark Jobs Leveraging the Spark GraphX API Other Books You May Enjoy

Faster average computations with aggregate

In the previous section, we saw how we can use map and reduce to calculate averages. Let's now look at faster average computations with the aggregate function. You can refer to the documentation mentioned in the previous section.

The aggregate is a function that takes three arguments, none of which are optional.

The first one is the zeroValue argument, where we put in the base case of the aggregated results.

The second argument is the sequential operator (seqOp), which allows you to stack and aggregate values on top of zeroValue. You can start with zeroValue, and the seqOp function that you feed into aggregate takes values from your RDD, and stacks or aggregates it on top of zeroValue.

The last argument is combOp, which stands for combination operation, where we simply take the zeroValue argument that is now aggregated through the...

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